To reproduce the code throughout this tutorial you only need to load the ggplot2 package

library(ggplot2)

Ggplot2 also comes with a number of built-in data sets! This tutorial will (mostly) use the ggplot2 provided Midwest dataset, which is a data frame that contains demographic information of Midwest counties from 2000 US census.

Grammar of Graphics:

The philosophy of ggplot2 is based on the Grammar of Graphics, which adapted by Hadley Wickham to describe the components of a plot. In this framework, a plot at its most basic must be composed of:

The Grammar of Graphics also describes more complex (optional) plot components such as:

Each of these components can be controlled with unique commands in ggplot2.



A basic plot:

In order to create a plot, you:

  1. Call the ggplot() function which creates a blank canvas
ggplot(data = midwest)

  1. Specify aesthetic mappings, which specifies how you want to map variables to visual aspects. In this case we are simply mapping the percbelowpoverty (percent of residents of the county below the poverty line) and perchsd (percent of residents of the county with a highschool diploma) variables to the x- and y-axes respectively.
ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd))

Note that despite specifying the data and the axes, the plot itself is empty.

  1. Add a layer that specifies which type of geometric object will show up on the plot
ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point()

Notice the + in the above code block. You will always use the + operator to add new layers onto your visualization.



Aesthetics:

Aesthetic mappings (arguments within the aes() function) take properties of the data and use them to influence visual characteristics of the plotted data, such as position, color, size, shape, or transparency value (alpha)

For example, we can add a mapping from the state of the counties to a color characteristic:

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state))


If you wish to apply an aesthetic property to an entire geometry (instead of mapping a characteristic of the data with the aesthetic), you can set that property as an argument to the geom method, outside of the aes() call

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(color = "blue")


All geometry types will leverage the aesthetic mappings supplied but the specific visual properties that the data of a specific geometry type will map to will vary. For example, you can map data to the shape of a geom_point (e.g., if they should be circles or squares)

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(shape = state), color = "blue") # Note the use of an aesthetic call that maps data to the shape is within the aes() call, while another aesthetic which regardless of the data, makes all points blue, is called outside of the aes() call


or you can map data to the linetype of a geom_line or geom_smooth (e.g., if it is solid or dotted)

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_smooth(aes(linetype = state), color = "black", alpha = .3) # Note like above, the specification of the lintype is based on the data and is therefore within the aes() call while the color and transparency value (alpha) is outside of the aes() call
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'


but not vice versa (as you can see below, ggplot will ouput a warning when you try to apply an impossible aesthetic to a geometry type)

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(linetype = state)) 
## Warning in geom_point(aes(linetype = state)): Ignoring unknown aesthetics:
## linetype


Most geoms require specifying an x and y mapping within aes() at the bare minimum, but some geoms (like geom_histogram, geom_density, and geom_bar) require either an x or a y

ggplot(data = midwest, aes(x = percbelowpoverty)) + #No y specified!
  geom_histogram() 
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


You can also easily add multiple geometries to a plot, allowing you to create complex graphics showing multiple aspects of your data at once!

ggplot(midwest, aes(x = percbelowpoverty, y = perchsd)) + 
  geom_point() + 
  geom_smooth() #geom_smooth is a function that calculates and then plots a simple linear model of your data
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'


The aesthetics for each geom can be different, so you could show multiple lines on the same plot (or with different colors, styles, etc). It’s also possible to give each geom a different data argument, so that you can show multiple data sets in the same plot.

For example, we can plot both points and a smoothed line for the same x and y variable but specify unique color and alpha values within each geom:

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(color = "blue") +
    geom_smooth(color = "black", alpha = .3)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'


If we specify an aesthetic within ggplot it will be passed on to each geom that follows.

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd, color = state)) +
  geom_point() +
  geom_smooth() 
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'


Or we can specify certain aes within each geom, which allows us to only show certain characteristics for that specific layer (i.e. geom_point).

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state)) +
  geom_smooth() 
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Other arguments:

These are all components of the grammar of graphics but will only be covered in brief. Extended discussions of them can be found online; see for example the Visualization chapter of Hadley’s R for Data Science book.


Scales

Whenever you specify an aesthetic mapping, ggplot uses a particular scale to determine the range of values that the data should map to. Thus when you specify:

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state))


ggplot automatically adds a scale for each mapped aesthetic (percbelowpoverty, percbelowpoverty, state) to the plot

Each scale can be explicitly represented by a function with the following name: scale_, followed by the name of the aesthetic property, followed by an _ and the name of the scale.

A continuous scale will handle things like numeric data (where there is a continuous set of numbers), whereas a discrete scale will handle things like colors (since there is a small list of distinct colors).

For example, the scale ggplot automatically applied to the above graph can be explicitly called and the same plot will be created:

# same as above, with explicit scales
ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state)) +
  scale_x_continuous() + # x is mapped from the percbelowpoverty variable which is continuous
  scale_y_continuous() + # y is mapped from the perchsd variable which is continuous
  scale_colour_discrete() # color is mapped from the state variable which is discrete

While the default scales will generally work fine, it is possible to explicitly add different scales to replace the defaults. For example, you can use a scale to change the direction of an axis:

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state)) +
  scale_x_reverse() +
  scale_y_reverse()

Similarly, you can use scale_x_log10() and scale_x_sqrt() to transform your scale or you can use the trans= argument within scale_x_continuous and scale_y_continuous to do the same thing:

# initial plot, non-transformed axis
ggplot(data = midwest, aes(x = poptotal, y = perchsd)) +
  geom_point()

# transform x axis using scale_x_log10
ggplot(data = midwest, aes(x = poptotal, y = perchsd)) +
  geom_point() +
  scale_x_log10()

# transform x axis using scale_x_continuous
ggplot(data = midwest, aes(x = poptotal, y = perchsd)) +
  geom_point() +
  scale_x_continuous(trans = "log10")

A common parameter to change is which set of colors to use in a plot. While you can use the default coloring, a more common option is to leverage the pre-defined palettes from viridis. These color sets have been carefully designed to look good and to be viewable to people with certain forms of color blindness. We can leverage these palettes by specifying the scale_color_viridis() function.

Note that you can get the possible palette names from this viridis website.

Also note that viridis has their own package and functions within that package that can be used within ggplot

#default color palette
ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = percollege)) 

#the default viridis color palette
ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = percollege)) +
  scale_color_viridis_c() # call scale_color_viridis command from the viridis package

#calling viridis color palette from the viridis package
ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = percollege)) +
  viridis::scale_color_viridis(discrete = F) # call scale_color_viridis command from the viridis package

You can also manually specify the colors you want to use as a named vector (typically done for discrete data):

#default color palette
ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state)) 

#the default viridis color palette
ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state)) +
  scale_color_manual(values = c("red", "orange", "green", "blue", "purple")) # assign a vector of colors for the plot, order of colors will match the order of the variable being mapped (typically alphabetical)

Here is a helpful image that I constantly refer to when trying to customize my colors:


Coordinate systems

The next term from the Grammar of Graphics that can be specified is the coordinate system. As with scales, coordinate systems are specified with functions that all start with coord_ and are added as a layer. There are a number of different possible coordinate systems to use, including:

  • coord_cartesian the default cartesian coordinate system, where you specify x and y values (e.g. allows you to zoom in or out).
  • coord_flip a cartesian system with the x and y flipped
  • coord_fixed a cartesian system with a “fixed” aspect ratio (e.g., 1.78 for a “widescreen” plot)
  • coord_polar a plot using polar coordinates
  • coord_quickmap a coordinate system that approximates a good aspect ratio for maps. See documentation for more details.

Some examples:

Zoom in with coord_cartesian

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state)) +
  coord_cartesian(xlim = c(0, 10)) # only show the x axis from value of 0 to value of 10 (but all points are still plotted, so some will show up cut off)

Flip x and y axis with coord_flip

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state)) +
  coord_flip()

Facets

Facets are ways of grouping a data plot into multiple different pieces (subplots). This allows you to view a separate plot for each value in a categorical variable.

Similar to scales and coordinate systems, different types of faceting are available and following the naming patter facet_ followed by the type of facet.

Within the argument of the facet_ function, you need to specify what variable to facet around with the tilde ~ followed by the variable name.

One of the possible functions to facet data is the facet_wrap() function. This will produce rows of subplots, one subplot for each categorical variable (the number of rows can be specified with the nrow argument:

ggplot(data = midwest, aes(x = percbelowpoverty, y = perchsd)) +
  geom_point(aes(color = state)) +
  facet_wrap(~state, nrow = 3)

You can use facet_grid to facet your data into a grid rather than a row. This is most common when you want to facet by more than one categorical variable. With facet_grid the variable to the left of the tilde (“~”) will be represented in the rows and the variable to the right will be represented across the columns.

ggplot(mpg) + 
  geom_histogram(aes(x = hwy)) + 
  facet_grid(class ~ cyl)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Position

Position The position argument is used to tweak how certain aspects of the plots are displayed. Its use depends heavily on the type of plot. For each geom, some positions will work, some will do nothing, and some will produce nonsense. They are most commonly used when trying to create grouped plots.

For example, we can look at a histogram of car mpg (hwy).

ggplot(mpg, aes(x = hwy, fill = class)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

If we add fill = class, it will group by class. The default is position = "stack"; let’s see what each does.

ggplot(mpg, aes(x = hwy, fill = class)) +
  geom_histogram(position = "stack")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(mpg, aes(x = hwy, fill = class)) +
  geom_histogram(position = "identity")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(mpg, aes(x = hwy, fill = class)) +
  geom_histogram(position = "dodge")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(mpg, aes(x = hwy), fill = class) +
  geom_histogram(position = "fill")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 5 rows containing missing values (`geom_bar()`).

In general:

  • identity is useful when you want to plot things exactly as they are.
  • stack is useful when you want to look both at overall values and per-group values.
  • dodge is useful for group comparisons.
  • fill is useful for considering percentages instead of counts.
  • jitter is useful for scatter plots (and similar) when multiple values may be placed at the same point.

Positions are generally a trial-and-error procedure. If the default isn’t sufficient, see if the others look/work better.

Stat

We can work through this another day if you’re interested!